Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Niche syndromes reveal climate-driven extinction threat to island endemic conifers

Nature Climate Change volume 9, pages 627–631 (2019)Cite this article

Subjects

Abstract

Anthropogenic climate change is predicted to cause many extinctions worldwide1. Although species endemic to islands or archipelagos have high conservation value and are vulnerable to human impacts2,3, there has been no global analysis of climate-driven extinction risk focused on island endemics. Here, we use conifers as a model system to assess extinction risk among island endemics under climate projections for 2070. We employ the emerging technique of combining native and non-native occurrence data to model climatic conditions under which each species can sustain a population4,5,6,7 and also incorporate horticultural data to model the broader range of conditions that allow short-term survival. Our projections indicate that some species will retain suitable climatic conditions, some will experience conditions completely precluding survival and others will experience intermediate-risk conditions that lead to population decline and eventual extinction. Based on different climate change models, we report island size thresholds of 400 to 20,000 km2, below which extinction risks increase. These patterns are driven by correlations among island area and the breadth of species’ realized, fundamental and tolerance niches. Notably, realized and fundamental niche breadth are positively correlated. Our results highlight management interventions needed to protect species from climate-driven extinction across islands of different sizes.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Learn more

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Occurrence data and niche models for three exemplar species.
Fig. 2: Island size and climate change severity determine the proportion of species remaining within their fundamental niches, as well as management needed to avoid climate-driven extinction.
Fig. 3: Correlations among island area, species’ realized and fundamental niche breadth and total available climate space.

Data availability

Some herbaria and consortia we queried for species occurrence data do not allow dissemination of their data beyond the user, so we are unable to publish the portion of our dataset derived from these online sources. However, these data are publicly available for download, so we provide information on where to access data from each herbarium and consortium in the references. For full details on herbarium consortia, including lists of individual participating herbaria whose data we used, see Supplementary Note 2. We also provide in the Supplementary Data a version of our species occurrence dataset that contains only the data derived from personal communication and our primary literature search. CHELSA climate data are publicly available at http://www.chelsa-climate.org and at the Dryad Digital Repository at https://doi.org/10.5061/dryad.kd1d4.

References

  1. Urban, M. C. Accelerating extinction risk from climate change. Science 348, 571–573 (2015).

    Article  CAS  Google Scholar 

  2. Kier, G. et al. A global assessment of endemism and species richness across island and mainland regions. Proc. Natl Acad. Sci. USA 106, 9322–9327 (2009).

    Article  CAS  Google Scholar 

  3. Fordham, D. A. & Brook, B. W. Why tropical island endemics are acutely susceptible to global change. Biodivers. Conserv. 19, 329–342 (2010).

    Article  Google Scholar 

  4. Sax, D. F., Early, R. & Bellemare, J. Niche syndromes, species extinction risks, and management under climate change. Trends Ecol. Evol. 28, 517–523 (2013).

    Article  Google Scholar 

  5. Booth, T. H. Using a global botanic gardens database to help assess the capabilities of rare eucalypt species to cope with climate change. Int. For. Rev. 17, 259–268 (2015).

    Google Scholar 

  6. Bocsi, T. et al. Plants’ native distributions do not reflect climatic tolerance. Divers. Distrib. 22, 615–624 (2016).

    Article  Google Scholar 

  7. Perret, D. L., Leslie, A. B. & Sax, D. F. Naturalized distributions show that climatic disequilibrium is structured by niche size in pines (Pinus L.). Glob. Ecol. Biogeogr. 28, 429–441 (2019).

    Google Scholar 

  8. Harter, D. E. V. et al. Impacts of global climate change on the floras of oceanic islands: projections, implications and current knowledge. Perspect. Plant Ecol. Syst. 17, 160–183 (2015).

    Article  Google Scholar 

  9. Ferreira, M. T. et al. Effects of climate change on the distribution of indigenous species in oceanic islands (Azores). Clim. Change 138, 603–615 (2016).

    Article  CAS  Google Scholar 

  10. Fortini, L. et al. A Landscape-Based Assessment of Climate Change Vulnerability for all Native Plants Technical Report No. HCSU-044 (University of Hawai’i, 2013).

  11. Booth, T. H. Assessing species climatic requirements beyond the realized niche: some lessons mainly from tree species distribution modeling. Clim. Change 145, 259–271 (2017).

    Article  Google Scholar 

  12. Guisan, A., Petitpierre, B., Broennimann, O., Daehler, C. & Kueffer, C. Unifying niche shift studies: insights from biological invasions. Trends Ecol. Evol. 29, 260–269 (2014).

    Article  Google Scholar 

  13. Early, R. & Sax, D. F. Climatic niche shifts between species’ native and naturalized ranges raise concern for ecological forecasts during invasions and climate change. Glob. Ecol. Biogeogr. 23, 1356–1365 (2014).

    Article  Google Scholar 

  14. Faurby, S. & Araujo, M. B. Anthropogenic range contractions bias species climate change forecasts. Nat. Clim. Change 8, 252–256 (2018).

    Article  Google Scholar 

  15. Li, Y. M., Liu, X., Li, X. P., Petitpierre, B. & Guisan, A. Residence time, expansion toward the equator in the invaded range and native range size matter to climatic niche shifts in non-native species. Glob. Ecol. Biogeogr. 23, 1094–1104 (2014).

    Article  Google Scholar 

  16. Bush, A. et al. Truncation of thermal tolerance niches among Australian plants. Glob. Ecol. Biogeogr. 27, 22–31 (2018).

    Article  Google Scholar 

  17. Kambach, S. et al. Of niches and distributions: range size increases with niche breadth both globally and regionally but regional estimates poorly relate to global estimates. Ecography 41, 1–11 (2018).

    Article  Google Scholar 

  18. Hill, M. P., Gallardo, B. & Terblanche, J. S. A global assessment of climatic niche shifts and human influence in insect invasions. Glob. Ecol. Biogeogr. 26, 679–689 (2017).

    Article  Google Scholar 

  19. Farjon, A. & Filer, D. An Atlas of the World’s Conifers: An Analysis of Their Distribution, Biogeography, Diversity and Conservation Status (Brill, 2013).

  20. Richardson, D. M. & Rejmanek, M. Conifers as invasive aliens: a global survey and predictive framework. Divers. Distrib. 10, 321–331 (2004).

    Article  Google Scholar 

  21. Karger, D. N. et al. Data descriptor: climatologies at high resolution for the earth’s land surface areas. Sci. Data 4, 170122 (2017).

    Article  Google Scholar 

  22. Petitpierre, B., Broennimann, O., Kueffer, C., Daehler, C. & Guisan, A. Selecting predictors to maximize the transferability of species distribution models: lessons from cross-continental plant invasions. Glob. Ecol. Biogeogr. 26, 275–287 (2017).

    Article  Google Scholar 

  23. Yu, F. Y. et al. Climatic niche breadth can explain variation in geographical range size of alpine and subalpine plants. Int. J. Geogr. Inf. Sci. 31, 190–212 (2017).

    Article  Google Scholar 

  24. Thuiller, W., Lavorel, S. & Araujo, M. B. Niche properties and geographical extent as predictors of species sensitivity to climate change. Glob. Ecol. Biogeogr. 14, 347–357 (2005).

    Article  Google Scholar 

  25. Dullinger, S. et al. Extinction debt of high-mountain plants under twenty-first century climate change. Nat. Clim. Change 2, 619–622 (2012).

    Article  Google Scholar 

  26. Cheke, A. & Hume, J. Lost Land of the Dodo (Yale Univ. Press, 2008).

  27. Holt, R. D., Barfield, M. & Gomulkiewicz, R. in Species Invasions: Insights Into Ecology, Evolution, and Biogeography (eds Sax, D. F. et al.) 259–290 (Sinauer Associates Inc., 2005).

  28. Skelly, D. K. et al. Evolutionary responses to climate change. Conserv. Biol. 21, 1353–1355 (2007).

    Article  Google Scholar 

  29. Lesk, C., Coffel, E., D’Amato, A. W., Dodds, K. & Horton, R. Threats to North American forests from southern pine beetle with warming winters. Nat. Clim. Change 7, 713–717 (2017).

    Article  Google Scholar 

  30. Dobrowski, S. Z. A climatic basis for microrefugia: the influence of terrain on climate. Glob. Change Biol. 17, 1022–1035 (2011).

    Article  Google Scholar 

  31. Farjon, A. Conifers of the World BRAHMS Database (RBG Kew, accessed 30 October 2017); https://herbaria.plants.ox.ac.uk/bol/conifers

  32. SERNEC Data Portal (SouthEast Regional Network of Expertise and Collections, accessed 9 September 2017); http://sernecportal.org/portal/index.php

  33. CNH Data Portal (Consortium of Northeastern Herbaria, accessed 20 September 2017); www.neherbaria.org

  34. Consortium of Pacific Northwest Herbaria Specimen Database (CPNWH) (Consortium of Pacific Northwest Herbaria, accessed 23 September 2017); www.pnwherbaria.org

  35. The C. V. Starr Virtual Herbarium (New York Botanical Garden, accessed 26 September 2017); http://sweetgum.nybg.org/science/vh/

  36. Botany Collection Search: Information Provided with the Permission of the National Museum of Natural History (Smithsonian Institution, accessed 2 October 2017); https://collections.nmnh.si.edu/search/botany/

  37. Tropicos: Botanical Information System at the Missouri Botanical Garden (Missouri Botanical Garden, accessed 7 October 2017); www.tropicos.org

  38. Harvard University Herbarium Catalogue (HUH, accessed 11 October 2017); http://kiki.huh.harvard.edu/databases/specimen_index.html

  39. Consortium of California Herbaria (CCH) Catalogue (accessed 14 October 2017). Consortium of California Herbaria; http://ucjeps.berkeley.edu/consortium

  40. University of Michigan Herbarium (accessed 16 October 2017). University of Michigan Herbarium; https://michiganflora.net/specimen-search.aspx

  41. The Australasian Virtual Herbarium (Council of Heads of Australasian Herbaria, accessed 16 October 2017); http://avh.chah.org.au

  42. Allan Herbarium Specimen Data (Manaaki Whenua/Landcare Research, accessed 30 October 2017); http://scd.landcareresearch.co.nz

  43. Botany Collections Online. (Auckland Museum, accessed 3 November 2017). Auckland Museum; http://www.aucklandmuseum.com/discover/collections-online

  44. NZFRI Online Dataset (Scion, accessed 6 November 2017). Scion; nzfri.scionresearch.com

  45. Botanical Database of Southern Africa (BODATSA) (SANBI. South African National Biodiversity Institute, accessed 9 November 2017). South African National Biodiversity Institute; http://newposa.sanbi.org/

  46. The Herbarium Catalogue, Royal Botanic Gardens, Kew (Kew, accessed 10 November 2017). Kew; http://www.kew.org/herbcat

  47. Royal Botanic Garden Edinburgh Herbarium Catalogue (RBGE, accessed 15 November 2017). Royal Botanic Garden Edinburgh; http://data.rbge.org.uk/search/herbarium/

  48. Muséum National d’Histoire Naturelle Catalogue (MNHN, accessed 20 November 2017). Muséum National d’Histoire Naturelle; https://science.mnhn.fr/institution/mnhn/collection/p/item/search/form

  49. Swedish Naturhistoriska Riksmuseet Catalogue (NRM, accessed 28 November 2017). Swedish Naturhistoriska Riksmuseet; http://herbarium.nrm.se/

  50. Baard, J. A. & Kraaij, T. Alien flora of the Garden Route National Park, South Africa. S. Afr. J. Bot. 94, 51–63 (2014).

    Article  Google Scholar 

  51. Badalamenti, E., LaMantia, T. & Pasta, S. Primo caso di naturalizzazione di Pinus canariensis C. Sm. (Pinaceae) per la Sicilia e prima stazione di Acacia cyclops G. Don (Fabaceae) sull’isola maggiore. Nat. Sicil. 37, 497–503 (2013).

    Google Scholar 

  52. Borges, P. A. V. et al. (eds) A List of the Terrestrial and Marine Biota from the Azores (Princípia, Cascais, 2010).

  53. Celesti-Grapow, L. et al. Inventory of the non-native flora of Italy. Plant Biosyst. 143, 386–440 (2009).

    Article  Google Scholar 

  54. Cronk, Q. C. B. The past and present vegetation of St. Helena. J. Biogeogr. 16, 47–64 (1989).

    Article  Google Scholar 

  55. Daehler, C. C. & Baker, R. F. New records of naturalized and naturalizing plants around Lyon Arboretum, Manoa Valley, O’ahu. Occas. Pap. Bernice Pauahi Bish. Mus. 87, 3–18 (2006).

    Google Scholar 

  56. Firsov, G. A. & Byalt, V. V. Review of woody exotic species producing self-seeding in St. Petersburg (Russia). Russ. J. Biol. Invasions 7, 84–104 (2016).

    Article  Google Scholar 

  57. Galasso, G. et al. An updated checklist of the vascular flora alien to Italy. Plant Biosyst. 152, 179–303 (2018).

    Article  Google Scholar 

  58. Hall, C. R. et al. Plant records. Watsonia 17, 183–198 (1988).

    Google Scholar 

  59. Heenan, P. B., de Lange, P. J., Cameron, E. K. & Champion, P. D. Checklist of dicotyledons, gymnosperms, and pteridophytes naturalised or casual in New Zealand: additional records 1999–2000. N. Z. J. Bot. 40, 155–174 (2002).

    Article  Google Scholar 

  60. Henderson, L. & Wilson, J. R. U. Changes in the composition and distribution of alien plants in South Africa: an update from the Southern African Plant Invaders Atlas. Bothalia 47, a2172 (2017).

    Article  Google Scholar 

  61. Hill, K. D. in Flora of Australia https://go.nature.com/30o2ZpJ (Australian Biological Resources Study, Department of the Environment and Energy, 2017).

  62. Kafle, G. & Savillo, I. T. Present status of Ramsar sites in Nepal. Int. J. Biodivers. Conserv. 1, 146–150 (2009).

    Google Scholar 

  63. Kondo, T. & Tsuyuzaki, S. Natural regeneration patterns of the introduced larch, Larix kaempferi (Pinaceae), on the volcano Mount Koma, northern Japan. Divers. Distrib. 5, 223–233 (1999).

    Article  Google Scholar 

  64. Lambdon, P. & Darlow, A. Botanical Survey of Ascension Island and St. Helena (Royal Society for the Protection of Birds, 2008).

  65. Lau, A. & Frohlich, D. New plant records for the Hawaiian Islands 2011–2012. Occas. Pap. Bernice Pauahi Bish. Mus. 114, 5–16 (2013).

    Google Scholar 

  66. de Lima, R. F. Land-Use Management and the Conservation of Endemic Species in the Island of São Tomé. PhD thesis, Lancaster Univ. (2012).

  67. de Lima, R. F. et al. Distribution and habitat associations of the critically endangered bird species of São Tomé Island (Gulf of Guinea). Bird Conserv. Int. 27, 455–469 (2017).

  68. Morita, S. et al. Unusually high levels of bio-available phosphate in the soils of Ogasawara Islands, Japan: putative influence of seabirds. Geoderma 160, 155–164 (2010).

    Article  CAS  Google Scholar 

  69. Oppenheimer, H. L. The spread of gymnosperms on Maui: a neglected element of the modern Hawaiian flora. Occas. Pap. Bernice Pauahi Bish. Mus. 68, 19–23 (2002).

    Google Scholar 

  70. Oppenheimer, H. L. New plant records from Moloka’i, Läna’i, Maui, and Hawai’i for 2006. Occas. Pap. Bernice Pauahi Bish. Mus. 96, 17–34 (2007).

    Google Scholar 

  71. Oppenheimer, H. L. New Hawaiian plant records for 2007. Occas. Pap. Bernice Pauahi Bish. Mus. 100, 22–38 (2008).

    Google Scholar 

  72. Rouget, M., Richardson, D. M., Milton, S. J. & Polakow, D. Predicting invasion dynamics of four alien Pinus species in a highly fragmented semi-arid shrubland in South Africa. Plant Ecol. 152, 79–92 (2001).

    Article  Google Scholar 

  73. Shimizu, Y. & Tabata, H. Invasion of Pinus lutchuensis and its influence on the native forest on a Pacific Island. J. Biogeogr. 12, 195–207 (1985).

    Article  Google Scholar 

  74. Singh, R. S., Rahlan, P. K. & Singh, S. P. Phytosociological and population structure of oak-mixed conifer forest in a part of Kumaun Himalaya. Environ. Ecol. 5, 475–487 (1987).

    Google Scholar 

  75. Webb, C. J., Sykes, W. R. & Garnock-Jones, P. J. Naturalised Pteridophytes, Gymnosperms, Dicotyledons. Flora of New Zealand Vol. 4 (Botany Division, DSIR, 1988).

  76. Woo, E. The Role of Plant-Bird Interactions in the Invasion of Juniperus bermudiana in Hawaii: Integrating Experiments, Behavior, and Models (State University of New York at Stony Brook, ProQuest Dissertations Publishing, 2008).

  77. Beck, J., Ballesteros-Mejia, L., Nagel, P. & Kitching, I. J. Online solutions and the ‘Wallacean shortfall’: what does GBIF contribute to our knowledge of species’ ranges? Divers. Distrib. 19, 1043–1050 (2013).

    Article  Google Scholar 

  78. Broennimann, O. et al. Measuring ecological niche overlap from occurrence and spatial environmental data. Glob. Ecol. Biogeogr. 21, 481–497 (2012).

    Article  Google Scholar 

  79. Qiao, H. J., Escobar, L. E., Saupe, E. E., Ji, L. Q. & Soberon, J. A cautionary note on the use of hypervolume kernel density estimators in ecological niche modelling. Glob. Ecol. Biogeogr. 26, 1066–1070 (2017).

    Article  Google Scholar 

  80. McSweeney, C. F., Jones, R. G., Lee, R. W. & Rowell, D. P. Selecting CMIP5 GCMs for downscaling over multiple regions. Clim. Dynam. 44, 3237–3260 (2014).

    Article  Google Scholar 

  81. R Core Team R: A Language and Environment for Statistical computing (R Foundation for Statistical Computing, 2017); https://www.R-project.org

  82. Hijmans, R. J. raster: Geographic Data Analysis and Modeling. R version 2.6-7 https://CRAN.R-project.org/package=raster(2017).

  83. QGIS Development Team QGIS Geographic Information System (Open Source Geospatial Foundation Project, 2017); http://qgis.osgeo.org

  84. Leutner, B. & Horning, N. RStoolbox: tools for remote sensing data analysis. R version 0.1.10 https://CRAN.R-project.org/package=RStoolbox (2017).

  85. Calenge, C. The package adehabitat for the R software: a tool for the analysis of space and habitat use by animals. Ecol. Model. 197, 516–519 (2006).

    Article  Google Scholar 

  86. Bivand, R. & Rundel C. rgeos: interface to geometry engine - open source (‘GEOS’). R version 0.3-26 https://CRAN.R-project.org/package=rgeos (2017).

  87. Spiess, A. N. qpcR: modelling and analysis of real-time PCR data. R version 1.4-0 https://CRAN.R-project.org/package=qpcR (2014).

  88. Mazerolle, M. J. AICcmodavg: model selection and multimodel inference based on (Q)AIC(c). R version 2.1-1 https://cran.r-project.org/package=AICcmodavg (2017).

  89. Jackman, S. pscl: classes and methods for R developed in the Political Science Computational Laboratory. R version 1.5.2 https://github.com/atahk/pscl/ (2017).

  90. Henningsen, A. & Hamann, J. D. systemfit: a package for estimating systems of simultaneous equations in R. J. Stat. Softw. 23, 1–40 (2007).

    Article  Google Scholar 

  91. Hothorn, T., Bretz, F. & Westfall, P. Simultaneous inference in general parametric models. Biom. J. 50, 346–363 (2008).

    Article  Google Scholar 

  92. Jurasinski, G. & Retzer, V. simba: a collection of functions for similarity analysis of vegetation data. R version 0.3-5 https://CRAN.R-project.org/package=simba (2012).

  93. Becker, R. A., Wilks, A. R., Brownrigg, R., Minka, T. P. & Deckmyn, A. maps: draw geographical maps. R version 3.2.0 https://CRAN.R-project.org/package=maps (2017).

Download references

Acknowledgements

This manuscript benefitted from financial support from the Institute at Brown for Environment and Society. Species occurrence data were provided by L. Celesti-Grapow, R. de Lima, L. Henderson, G. Firsov, A. Khmarik, S. Peccenini, R. Wagensommer, J. Baard, D. Luscombe, B. Chaves, G. Dyer and P. Normandy. Many botanists and horticulturalists provided helpful replies to our requests for additional information regarding non-native species occurrences in our dataset.

Author information

Authors and Affiliations

  1. Department of Ecology and Evolutionary Biology, Brown University, Providence, RI, USA

    Kyle C. Rosenblad, Daniel L. Perret & Dov F. Sax

  2. Institute at Brown for Environment and Society, Brown University, Providence, RI, USA

    Kyle C. Rosenblad, Daniel L. Perret & Dov F. Sax

Authors
  1. Kyle C. Rosenblad
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Daniel L. Perret
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dov F. Sax
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

K.C.R. and D.F.S. designed the study and wrote the manuscript. K.C.R. collected the data and performed the analyses. D.L.P. helped design the data collection approach, contributed to some analyses, assisted in interpretation and contributed to writing the manuscript.

Corresponding author

Correspondence to Kyle C. Rosenblad.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information: Nature Climate Change thanks Carsten Külheim and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Figs. 1–5, Tables 1–2, methods and Supplementary Notes 1 and 2.

Reporting Summary

Supplementary Data 1

Species occurrence data derived from personal communication and our primary literature search.

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rosenblad, K.C., Perret, D.L. & Sax, D.F. Niche syndromes reveal climate-driven extinction threat to island endemic conifers. Nat. Clim. Chang. 9, 627–631 (2019). https://doi.org/10.1038/s41558-019-0530-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41558-019-0530-9

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing